Analysis and Integration of Belief and Observation Fusion for Multi-Robot Multi-Sensor Target Estimation

This paper presents the analysis and integration of two forms of fusion for multi-robot multi-sensor target estimation, namely observation fusion and belief fusion. While the observation fusion is a traditional sensor fusion where the observation likelihood is fused within the loop of recursive Bayesian estimation (RBE), the belief fusion fuses independently updated beliefs outside the RBE loop. The comparative analysis of the two fusions show that observation fusion is suited for multimodal sensors within a single robot whereas multiple robots can each update their belief more effectively through belief fusion. Numerical results validate the strength of each fusion approach and show that the effectiveness of the optimally integrated system.